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generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and DataGovernance application.
DataObservability and Data Quality are two key aspects of data management. The focus of this blog is going to be on DataObservability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.
IMPACT 2023- The DataObservability Summit (Virtual event – November 8) Focus on Data and AI : The summit will illuminate how contemporary technical teams are crafting impactful and performant data and AI products that businesses can rely on.
IMPACT is a great opportunity to learn from experts in the field, network with other professionals, and stay up-to-date on the latest trends and developments in data and AI. The summit will be held on November 8th, 2023.
The financial services industry has been in the process of modernizing its datagovernance for more than a decade. But as we inch closer to global economic downturn, the need for top-notch governance has become increasingly urgent. That’s why data pipeline observability is so important.
Alation and Bigeye have partnered to bring dataobservability and data quality monitoring into the data catalog. Read to learn how our newly combined capabilities put more trustworthy, quality data into the hands of those who are best equipped to leverage it.
And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data. Protected and compliant data.
We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. DataGovernanceDatagovernance is the foundation of any data quality framework. It primarily caters to large organizations with complex data environments.
The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. Having model-level data validations along with implementing a dataobservability framework helps to address the data vault’s data quality challenges.
While data fabric takes a product-and-tech-centric approach, data mesh takes a completely different perspective. Data mesh inverts the common model of having a centralized team (such as a dataengineering team), who manage and transform data for wider consumption. But why is such an inversion needed?
Understanding data mesh Data mesh is a decentralized architecture type that allows different departments to access data independently. It’s different from traditional data architecture, which usually has dedicated dataengineering teams that provide access to information after other departments request it.
Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, dataengineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for dataengineering and MLOps workflows.
Now that we’re in 2024, it’s important to remember that dataengineering is a critical discipline for any organization that wants to make the most of its data. These data professionals are responsible for building and maintaining the infrastructure that allows organizations to collect, store, process, and analyze data.
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